Airfoil Self-Noise
Donated on 3/3/2014
NASA data set, obtained from a series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections conducted in an anechoic wind tunnel.
Dataset Characteristics
Multivariate
Subject Area
Physics and Chemistry
Associated Tasks
Regression
Feature Type
Real
# Instances
1503
# Features
5
Dataset Information
Additional Information
The NASA data set comprises different size NACA 0012 airfoils at various wind tunnel speeds and angles of attack. The span of the airfoil and the observer position were the same in all of the experiments.
Has Missing Values?
No
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| frequency | Feature | Integer | Hz | no | |
| attack-angle | Feature | Binary | deg | no | |
| chord-length | Feature | Continuous | m | no | |
| free-stream-velocity | Feature | Continuous | m/s | no | |
| suction-side-displacement-thickness | Feature | Continuous | m | no | |
| scaled-sound-pressure | Target | Continuous | dB | no |
0 to 6 of 6
Additional Variable Information
This problem has the following inputs: 1. Frequency, in Hertzs. 2. Angle of attack, in degrees. 3. Chord length, in meters. 4. Free-stream velocity, in meters per second. 5. Suction side displacement thickness, in meters. The only output is: 6. Scaled sound pressure level, in decibels.
Dataset Files
| File | Size |
|---|---|
| airfoil_self_noise.dat | 58.6 KB |
Reviews
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset airfoil_self_noise = fetch_ucirepo(id=291) # data (as pandas dataframes) X = airfoil_self_noise.data.features y = airfoil_self_noise.data.targets # metadata print(airfoil_self_noise.metadata) # variable information print(airfoil_self_noise.variables)
Brooks, T., Pope, D., & Marcolini, M. (1989). Airfoil Self-Noise [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5VW2C.
Creators
Thomas Brooks
D. Pope
Michael Marcolini
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.